Suppose a student memorizes every answer in a practice paper.

If the final exam contains the same questions, the student may score 100%. But that does not prove understanding.

The same problem can happen in machine learning:

Model sees historical examples during training 
                ↓ 
Model memorizes those examples 
                ↓ 
Excellent score on familiar data 
                ↓ 
Poor predictions on new production data

• The business problem
• Prediction unit
• Prediction moment
• Label
• Baseline
• Business action

Today, we address the next question:

How can we determine whether the model learned a reusable pattern instead of memorizing its training data?

The answer is to evaluate it using examples it did not learn from.